VTC Pattern Similarity
ERS
- Quadratic age model is best-fitting
- Main effect of age (p < 0.01), regardless of controlling for
voxel number or removing 1 run subs
## Data: VTCers.long
## Models:
## VTCersd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCersd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## VTCersd1modelagelin 7 -635.77 -613.50 324.88 -649.77
## VTCersd1modelagesq 9 -639.19 -610.56 328.60 -657.19 7.4258 2 0.02441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 160.5749 1 < 2.2e-16 ***
## RewardType 1.5529 1 0.212712
## ageScaled 0.9232 1 0.336647
## ageScaledsq 7.0190 1 0.008065 **
## HighRewardStim 0.3076 1 0.579187
## RewardType:ageScaled 0.0256 1 0.872976
## RewardType:ageScaledsq 0.3526 1 0.552618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 160.1296 1 < 2.2e-16 ***
## RewardType 1.5529 1 0.212712
## ageScaled 0.8688 1 0.351278
## ageScaledsq 6.9375 1 0.008441 **
## HighRewardStim 0.3129 1 0.575930
## goldencvoxnumScaled 0.6727 1 0.412125
## RewardType:ageScaled 0.0256 1 0.872976
## RewardType:ageScaledsq 0.3526 1 0.552618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 134.2317 1 < 2e-16 ***
## RewardType 0.8491 1 0.35679
## ageScaled 0.4723 1 0.49193
## ageScaledsq 3.9887 1 0.04581 *
## HighRewardStim 1.0225 1 0.31192
## RewardType:ageScaled 0.1597 1 0.68940
## RewardType:ageScaledsq 0.4044 1 0.52482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encoding similarity
- Linear age model is best-fitting
- Main effects of reward level (p < 0.001) and age (p < 0.05),
regardless of controlling for voxel number or removing 1 run subs
## Data: VTCencsim.long
## Models:
## VTCencsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCencsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df
## VTCencsimd1modelagelin 7 -434.15 -413.27 224.08 -448.15
## VTCencsimd1modelagesq 9 -431.66 -404.81 224.83 -449.66 1.5103 2
## Pr(>Chisq)
## VTCencsimd1modelagelin
## VTCencsimd1modelagesq 0.4699
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 257.9367 1 < 2.2e-16 ***
## RewardType 13.8480 1 0.0001982 ***
## ageScaled 4.3820 1 0.0363212 *
## HighRewardStim 1.3402 1 0.2469974
## RewardType:ageScaled 0.1209 1 0.7280601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 257.4219 1 < 2.2e-16 ***
## RewardType 13.8480 1 0.0001982 ***
## ageScaled 3.8598 1 0.0494559 *
## HighRewardStim 1.3290 1 0.2489831
## goldencvoxnumScaled 0.7466 1 0.3875695
## RewardType:ageScaled 0.1209 1 0.7280601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 257.9367 1 < 2.2e-16 ***
## RewardType 13.8480 1 0.0001982 ***
## ageScaled 4.3820 1 0.0363212 *
## HighRewardStim 1.3402 1 0.2469974
## RewardType:ageScaled 0.1209 1 0.7280601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Retrieval similarity
- Linear age model is best-fitting
- No effects
## Data: VTCretsim.long
## Models:
## VTCretsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCretsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df
## VTCretsimd1modelagelin 7 -530.23 -509.34 272.11 -544.23
## VTCretsimd1modelagesq 9 -530.35 -503.50 274.17 -548.35 4.1215 2
## Pr(>Chisq)
## VTCretsimd1modelagelin
## VTCretsimd1modelagesq 0.1274
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 378.3351 1 <2e-16 ***
## RewardType 0.9268 1 0.3357
## ageScaled 1.4320 1 0.2314
## HighRewardStim 1.4449 1 0.2293
## RewardType:ageScaled 0.0286 1 0.8656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anterior Hippocampus Pattern Similarity LEFT OFF HERE
ERS
## Data: anthippers.long
## Models:
## anthippersd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippersd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df
## anthippersd1modelagelin 7 -764.18 -741.91 389.09 -778.18
## anthippersd1modelagesq 9 -763.92 -735.28 390.96 -781.92 3.7401 2
## Pr(>Chisq)
## anthippersd1modelagelin
## anthippersd1modelagesq 0.1541
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 2.4476 1 0.117707
## RewardType 1.5299 1 0.216122
## ageScaled 1.5259 1 0.216727
## HighRewardStim 0.0029 1 0.956885
## RewardType:ageScaled 9.4089 1 0.002159 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 2.4261 1 0.119327
## RewardType 1.5299 1 0.216122
## ageScaled 1.5405 1 0.214549
## HighRewardStim 0.0033 1 0.954171
## goldencvoxnumScaled 0.2511 1 0.616322
## RewardType:ageScaled 9.4089 1 0.002159 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 1.0369 1 0.3086
## RewardType 1.1451 1 0.2846
## ageScaled 0.1238 1 0.7250
## HighRewardStim 0.0297 1 0.8632
## RewardType:ageScaled 4.9365 1 0.0263 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encoding similarity
## Data: anthippencsim.long
## Models:
## anthippencsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippencsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df
## anthippencsimd1modelagelin 7 -731.95 -711.07 372.98 -745.95
## anthippencsimd1modelagesq 9 -729.11 -702.26 373.55 -747.11 1.1562 2
## Pr(>Chisq)
## anthippencsimd1modelagelin
## anthippencsimd1modelagesq 0.561
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 26.1044 1 3.234e-07 ***
## RewardType 0.3252 1 0.5685
## ageScaled 0.2013 1 0.6537
## HighRewardStim 0.9699 1 0.3247
## RewardType:ageScaled 1.0964 1 0.2951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 27.5386 1 1.54e-07 ***
## RewardType 0.3252 1 0.5685
## ageScaled 0.4009 1 0.5266
## HighRewardStim 0.9004 1 0.3427
## goldencvoxnumScaled 1.6902 1 0.1936
## RewardType:ageScaled 1.0964 1 0.2951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 32 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 32 rows containing missing values or values outside the scale range
## (`geom_line()`).
Retrieval similarity
## Data: anthippretsim.long
## Models:
## anthippretsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippretsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
## npar AIC BIC logLik deviance Chisq Df
## anthippretsimd1modelagelin 7 -690.46 -669.57 352.23 -704.46
## anthippretsimd1modelagesq 9 -689.77 -662.92 353.89 -707.77 3.3117 2
## Pr(>Chisq)
## anthippretsimd1modelagelin
## anthippretsimd1modelagesq 0.1909
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 28.2522 1 1.065e-07 ***
## RewardType 0.8867 1 0.3464
## ageScaled 0.1883 1 0.6643
## HighRewardStim 0.0311 1 0.8600
## RewardType:ageScaled 0.5084 1 0.4758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: similarity
## Chisq Df Pr(>Chisq)
## (Intercept) 28.9388 1 7.47e-08 ***
## RewardType 0.8867 1 0.3464
## ageScaled 0.0878 1 0.7669
## HighRewardStim 0.0217 1 0.8828
## goldretvoxnumScaled 0.8502 1 0.3565
## RewardType:ageScaled 0.5084 1 0.4758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 32 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 32 rows containing missing values or values outside the scale range
## (`geom_line()`).
Trialwise VTC ERS
Brain-Behavior & Brain-Brain Relations
##
## Call:
## lm(formula = highvlowspecificmemd2 ~ highvlowERSabs + VTC.highvlowencsim +
## ageScaled + HighRewardStim, data = anthippRSAdatawbehavfullruns)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.284679 -0.062186 0.004287 0.051144 0.217183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.024624 0.024368 -1.010 0.31585
## highvlowERSabs 2.698326 0.788071 3.424 0.00105 **
## VTC.highvlowencsim 0.841374 0.463675 1.815 0.07400 .
## ageScaled 0.001505 0.012110 0.124 0.90144
## HighRewardStimHigh Reward Cat - Place -0.003522 0.024083 -0.146 0.88415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1024 on 68 degrees of freedom
## Multiple R-squared: 0.2029, Adjusted R-squared: 0.156
## F-statistic: 4.328 on 4 and 68 DF, p-value: 0.003536
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = highvlowERS ~ VTA * ageScaled + HighRewardStim,
## data = anthippRSAdatawbehavfullruns)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072556 -0.017396 -0.000258 0.019100 0.058890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0007279 0.0049501 -0.147 0.8835
## VTA -0.0216526 0.0095308 -2.272 0.0263 *
## ageScaled 0.0073882 0.0040890 1.807 0.0752 .
## HighRewardStimHigh Reward Cat - Place 0.0021026 0.0066552 0.316 0.7530
## VTA:ageScaled 0.0068634 0.0112692 0.609 0.5445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02708 on 68 degrees of freedom
## Multiple R-squared: 0.1335, Adjusted R-squared: 0.08252
## F-statistic: 2.619 on 4 and 68 DF, p-value: 0.04243
## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = VTC.highvlowencsim ~ dlPFC * ageScaled + HighRewardStim,
## data = anthippRSAdatawbehavfullruns)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067777 -0.016758 -0.002033 0.012705 0.058763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0031037 0.0042450 0.731 0.467
## dlPFC 0.0116198 0.0026357 4.409 3.79e-05
## ageScaled -0.0030698 0.0033570 -0.914 0.364
## HighRewardStimHigh Reward Cat - Place 0.0016807 0.0056392 0.298 0.767
## dlPFC:ageScaled -0.0006188 0.0024077 -0.257 0.798
##
## (Intercept)
## dlPFC ***
## ageScaled
## HighRewardStimHigh Reward Cat - Place
## dlPFC:ageScaled
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02388 on 68 degrees of freedom
## Multiple R-squared: 0.2265, Adjusted R-squared: 0.181
## F-statistic: 4.977 on 4 and 68 DF, p-value: 0.001405
## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = dlPFC ~ VTA, data = anthippRSAdatawbehavfullruns)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2832 -0.6385 -0.1263 0.5527 3.7305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5456 0.1348 4.046 0.000131 ***
## VTA 0.8072 0.3467 2.328 0.022754 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.062 on 71 degrees of freedom
## Multiple R-squared: 0.07093, Adjusted R-squared: 0.05785
## F-statistic: 5.421 on 1 and 71 DF, p-value: 0.02275
## Data points may overlap. Use the `jitter` argument to add some amount of
## random variation to the location of data points and avoid overplotting.
